Online Multi-View Learning With Knowledge Registration Units

In this work, we investigate online multi-view learning according to the multi-view complementarity and consistency principles to memorably process online multi-view data when fused across views. Online diverse features through different deep feature extractors under different views are used as input to an online learning method to privately and memorably optimize in each view for the discovery and memorization of the view-specific information. More specifically, according to the multi-view complementarity principle, a softmax-weighted reducible (SWR) loss is proposed to selectively retain credible views and neglect incredible ones for the online model’s cross-view complementarity fusion. According to the multi-view consistency principle, we design a cross-view embedding consistency (CVEC) loss and a cross-view Kullback–Leibler (CVKL) divergence loss to maintain the cross-view consistency of the online model. Since the online multi-view learning setup needs to avoid repeatedly accessing online data to handle the knowledge forgetting in each view, we propose a knowledge registration unit (KRU) based on dictionary learning to incrementally register newly view-specific knowledge of online unlabeled data to the learnable and adjustable dictionary. Finally, by using the above strategies, we propose an online multi-view KRU approach and evaluate it with comprehensive experiments, thereby showing its superiority in online multi-view learning.